The risk for poor glycemic control in patients with type 2 diabetes can be predicted with confidence by using machine learning methods, a new study from Finland finds. The most important factors ...

University of Virginia School of Data Science researcher Heman Shakeri has been awarded a major new research grant to lead work at the intersection of machine learning and diabetes care. Shakeri will ...

Study of Over Three Million Patients for Risk of Type 2 Diabetes Demonstrates Potential for More Advanced Approach to Early Identification Over 60% of U.S. adults have risk factors for type 2 diabetes ...

To this point, determination of genetic risk for Type 1 diabetes mostly has been limited to people with well-known and well-documented risk profiles. But with a machine learning tool created by ...